Multimodal hyperspectral fluorescence and spatial frequency domain imaging for tissue health diagnostics of the oral cavity

Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review

Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption.

Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering

Significance

Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time.

Aim

A hyperspectral camera was used to capture 1216 × 1936   pixel wide-field reflectance images of in vivo human tissue at 205 wavelength bands from 420 to 830 nm.

Approach

The optical properties of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering were used with multi-layer Monte Carlo models to generate simulated diffuse reflectance spectra for 24,000 random combinations of physiologically relevant tissue components. These spectra were then used to train an artificial neural network (ANN) to predict tissue component concentrations from an input reflectance spectrum.

Results

The ANN achieved low root mean square errors in a test set of 6000 independent simulated diffuse reflectance spectra while calculating concentration values more than 4000× faster than a conventional iterative least squares approach.

Conclusions

In vivo finger occlusion and gingival abrasion studies demonstrate the ability of this approach to rapidly generate high-resolution images of tissue component concentrations from a hyperspectral dataset acquired from human subjects.

SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers

We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

Measuring changes in blood volume fraction during induced gingivitis of healthy and unhealthy populations using hyperspectral spatial frequency domain imaging: a clinical study

This investigation aimed to quantitatively measure the changes in inflammation of subjects with healthy and unhealthy gums during a period of induced gingivitis. A total of 30 subjects (15 healthy, 15 with gum inflammation) were enlisted and given oral exams by a dental hygienist. Baseline measurements were acquired before a 3-week period of oral hygiene abstinence. The lobene modified gingival index scoring was used for inflammation scoring and hyperspectral spatial frequency domain imaging was used to quantitatively measure oxy- and deoxygenated blood volume fraction at two time points: at Baseline and after 3 weeks of oral hygiene abstinence. We found that abstaining from oral hygiene causes a near proportional increase in oxygenated and deoxygenated blood volume fraction for healthy individuals. For individuals who started the study with mild to moderate gingivitis, increases in blood volume were mainly due to deoxygenated blood.